Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things.
Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.
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Table of Contents
1. An Introduction to Data Architecture 2. The End-State Architecture The "World Map" 3. Transformations in the End-State Architecture 4. A Brief History of Big Data 5. The Siloed Application Environment 6. Introduction to Data Vault 2.0 7. The Operational Environment: A Short History 8. A Brief History of Data Architecture 9. Repetitive Analytics: Some Basics 10. Nonrepetitive Data 11. Operational Analytics: Response Time 12. Operational Analytics 13. Personal Analytics 14. Data Models Across the End-State Architecture 15. The System of Record 16. Business Value and the End-State Architecture 17. Managing Text 18. An Introduction to Data Visualizations
